Although applicable to any system that uses control algorithms with parameters, the present invention will be described in combination with vehicles, and especially with parameters of driver assistance systems in vehicles.
In modern vehicles, a plurality of driver assistance systems can be provided to assist the driver in manoeuvring the vehicle.
For example, lane assistance systems can issue driver warnings if a vehicle is about to leave a driving lane without prior use of the turn lights or even provide a steering intervention to hold the vehicle in the driving lane.
Furthermore, cruise control systems can automatically detect vehicles in front of a vehicle and automatically accelerate and/or decelerate the vehicle to keep a predefined distance between the vehicles.
Further, driver assistance systems range from automatic parking functions to fully autonomous driving of a vehicle.
To provide such driver assistance systems, a detailed model of the vehicle's surroundings has to be established in the vehicle systems. This can, for example, be done by capturing video images of the vehicle's surroundings with mono or stereo video cameras and detecting objects in the video images.
Different algorithms are used for object detection, scene classification, and the like. Such algorithms are based on a plurality of parameters which can be individually tuned to match the desired behaviour.
One possibility is to use heuristics where possible scenarios are identified by problem analysis before the implementation of the algorithm. This solution uses heuristics to treat every single scenario and is thus limited to a finite number of scenarios.
An alternative is to use offline learning, where a model of the relevant scenarios is developed by problem analysis before the implementation of the algorithm. The algorithm is then trained offline, i.e. the parameters of the algorithm are tuned offline, to the most suited values. Using the models and a finite set of data the learning of the parameters is performed using ground truth information which is provided by labelling of the data. Such an algorithm can cope with an infinite number of scenarios. But the model parameters are frozen to the values learned before the release of the algorithm into the vehicle production.